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Three Things About Regression That Every Data Scientist Should Know
Understanding these three things will improve how you go about linear and generalized linear modeling
I consider myself more of a mathematician than a data scientist. I can’t bring myself to execute methods blindly, with no understanding of what’s going on under the hood. I have to get deep into the math to trust the results. That’s a good thing because it’s very easy nowadays to just run models and go home.
A model is only as good as your understanding of it, and I worry that a lot of people are running models and just accepting the first thing that comes out of them. When it comes to regression modeling — one of the most common forms of modeling out there — you’ll be a better data scientist if you can understand a few simple things about how these models work and why they are set up the way they are.
1. You are predicting an average — not an actual value
When you run a regression model, usually you are finding a relationship between the input variables and some sort of mean value related to the outcome. Let’s look at linear regression. When we run a linear regression we are making two very important assumptions about our outcome variable y:
- That the possible values of y for any given input variables are distributed around a mean.
- That the mean of y has an additive…
